Thoroughly revised and updated with the latest results, this Third
Edition provides an account of the theory and applications of linear
models. The authors present a unified theory of inference from linear
models and its generalizations with minimal assumptions. They not only
use least squares theory, but also alternative methods of estimation and
testing based on convex loss functions and general estimating equations.
Highlights include sensitivity analysis and model selection, an analysis
of incomplete data, and an analysis of categorical data based on a
unified presentation of generalized linear models. There is also an
extensive appendix on matrix theory that is particularly useful for
researchers in econometrics, engineering, and optimization theory. This
text is recommended for courses in statistics at the graduate level as
well as for other courses in which linear models play a role.